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Measuring Biases in judges using textual data

Introduction

Judges can suffer from behavioral biases that could affect their decision-making during part of their daily activities. This is especially important in developing countries such as Peru, where the public sector faces several issues when trying to provide an optimal provision of goods and services to its citizens. Hence, finding a way to measure them is key in order to show and mitigate the effects of such biases.

This project aims to tackle this challenge using natural language processing to obtain a measure of gender bias from publicly available data from judicial cases in Peru.

The pipeline of this repo does the following

  1. Create a parsed Corpus for each judge found on the sample for all the available years
  2. Create word embeddings for each judge using this data and Word2Vec
  3. Use the word embeddings to obtain two gender slants 3.1. Gender Career (Gender vs Career/Household oriented chores) 3.2. Gender Moral (Gender vs Good/Bad)

Note: The original idea comes from this Kenya paper which uses Glove instead of Word2Vec and doesn't work with textual data in spanish. As the original scripts didn't tackle those challenges and needed to be improved in terms of abstraction/softcoding/documentation, they were recreated from scratch and the final version is the one of this repository.

Statistical Overview

Obtaining the embeddings

The slants are obtained from word embeddings created for each judge using the judicial information of the cases they've worked on.

These embeddings were built using Word2Vec with the Continuous Bag of Words (CBOW) approach.

Creating the required dimensions

The embeddings for the 5 most common words associated to male and female concepts on the data were averaged to create the male and female dimension, respectively. Then, the gennder dimension is defined as the difference between the male and female dimensions, i.e.

$$\overrightarrow{gender} = \frac{ \sum male_{n} }{N} - \frac{ \sum female_{n} }{N}$$

Where math male_{n} and math female_{n} represent the n-th male/female word from each dimension

For the career slant, two dimensions were created: career and familiy; and, for the moral ones, good and bad.

Note: A more thorough explanation can be on page 41 of the Kenya paper.

Pipeline Overview

  • 1_create_judges_files.py cleans the scraped data and creates a unique dataset per judge for each available year (e.g. judge_1-2017.pkl, judge_1-2018.pkl, ...)
  • 2_judges_files_stacker.py stacks these datasets at the judge level (e.g. judge_1-2017.pkl and judge_1-2018.pkl is stacked to create judge_1.pkl)
  • 3_train_embeddings_w2v.py trains w2vec models per judge using the stacked datasets created by 2_judges_files_stacker.py and the word_dimension_tokens from parameters.json (explained on next section)

The functions to perform each of these tasks are defined on the respective .py files. However, there are subroutines used across the three files from the pipeline. These ones are stored in cleaning_scripts/corpus_cleaner_funcs.py or cleaning_scripts/ner_gender_detection.py depending on their functionality.

Note: The input comes from a private PSQL database where the data from this website was scraped.

Deployment

  • Create an environment to run the scripts
    • If you are using anaconda, install it using environment.yml
    • Else, create the environment using runtime.txt to find the right Python version and requirements.txt for the packages
  • Create the file parameters.json on the folder where you'll run either the .py or `.sh`` scripts
{
"paths": {
        "unix_paths": {"out_directory": "/burg/sscc/projects/data_text_iat/judges_data"}
	},
"parameters": {
        "year": "2018",
        "embeddings_file": "/burg/sscc/projects/data_text_iat/judges_data/SBW-vectors-300-min5.txt"
    },
"male_pronouns": ["él", "él mismo", "suyo", "sí", "consigo", "ese", "ese mismo", "aquel", "aquel mismo", "este", "este mismo", "esto", "aquello", "aquello mismo", "otro", "otro mismo", "alguno", "alguno mismo", "ninguno", "ninguno mismo", "varios", "varios mismos", "pocos", "pocos mismos", "muchos", "muchos mismos", "unos", "unos mismos", "mío", "tuyo", "nuestro", "vuestro", "cuyo", "cuántos", "cuánto", "cuantos", "cuanto", "todo", "tanto", "poco", "demasiado", "algunos", "todos", "tantos", "demasiados", "otros", "nosotros", "vosotros", "ellos", "el", "los", "míos", "tuyos", "nuestros", "vuestros", "suyos", "el que", "el cual", "los que", "los cuales", "cuyos", "mucho", "otro más", "cualquiera", "ambos", "sendos", "uno"],
"female_pronouns": ["nosotras", "vosotras", "ellas", "la", "las", "ella", "mía", "mías", "tuya", "tuyas", "nuestra", "nuestras", "vuestra", "vuestras", "suya", "suyas", "esta", "esa", "aquella", "la que", "la cual", "cuya", "cuanta", "las que", "las cuales", "cuyas", "cuantas", "cuánta", "cuántas", "alguna", "toda", "tanta", "poca", "demasiada", "otra", "mucha", "ninguna", "algunas", "todas", "tantas", "pocas", "demasiadas", "otras", "muchas", "varias", "otra más", "cualquiera", "ambas", "sendas", "una", "ella misma", "sí", "consigo", "esa misma", "aquella misma", "esta misma", "esto", "otra misma", "alguna misma", "ninguna misma", "varias mismas", "pocas mismas", "muchas mismas", "unas", "unas mismas"],
"word_dimension_tokens": {
    "male_names": ["luis", "juan", "carlos","antonio","miguel"],
    "female_names": ["rosa", "maría","pilar","isabel","ana"],
    "male": ["sr", "dr", "", "", ""],
    "female": ["ella", ""],
    "good": ["gran", "solidaria","sana","prudente","trabajadora", "razonabilidad"],
    "bad": ["agresor","morosos","mala","perjudicada","victima"],
    "career": ["pago", "trabajador","obreros", "empleadores", "obrero"],
    "family": ["familia","hijos","hija","padre", "madre"]
},
}
  • Run the scripts according to the order indicated at the beginning of the file
    • After running 1_create_judges_files.py and 2_judges_files_stacker.py, Update the entries male_pronouns, female_pronouns, word_dimension_tokens according to your data
    • An automated version of this classification was attempted in archive/2.5_obtain_words_per_category.py using a BERT model trained with spanish data. The results were highly inaccurate so the script was discarded.